Multiresponse optimization of WEDM parameters on machining 16MnCr5 alloy steel using Taguchi technique
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Abstract
In this present work, an attempt has been made to optimize the wire electric discharge machining using muliresponse optimization technique based on Taguchi’s design approach. 16MnCr5 Alloy steel was selected as workpiece material. The response parameters viz. material removal rate and surface roughness were optimized by varying types of electrode, pulse on time (T_{on}), pulse off time (T_{off}) and peak current (I_{p}). The Taguchi’s L_{18} mixed orthogonal array has been used for planning and designing the experiments. ANOVA was used to find the contribution and significance of different process parameters on the response parameters. The results clearly indicated that pulse on time (T_{on}) was the most influential factor for the material removal rate and surface roughness. Optimal level of process parameters was used to perform the confirmatory experiments which verified the improvements in performance characteristics.
Keywords
WEDM 16MnCr5 MRR SR Signal to noise (S/N) ratio ANOVA L_{18} OAList of symbols
 V_{c}
Cutting Speed
 M_{t}
Thickness of workpiece material (mm)
 W_{d}
Diameter of the wire (mm)
 T_{on}
Pulse on time
 T_{off}
Pulse off time
 I_{p}
Peak current
 WEDM
Wire electrical discharge machining
 MRR
Material removal rate
 SR
Surface roughness
 ANOVA
Analysis of variance
 S/N
Signal to noise ratio
1 Introduction
Important WEDM process parameters
Parameter  Definition  Effect 

Pulse on time T_{on}  It is the time duration of flow of current in each cycle  MRR is directly proportional to the amount of energy applied during pulse on time. Increase in pulse on time will lead to generation of more heat energy 
Pulse off time T_{off}  It is the duration of time between two simultaneous sparks and also called pulse interval  With a lower value of T_{off}, there is more number of discharges in a given time, resulting in increase in MRR 
Spark gap  It is the distance between the electrode and the workpiece during machining process.  The spark gap depends on the properties of material and electrode 
Peak current  It is the maximum value of the current passing through the electrodes for the given pulse  Increase in the peak current value increases the pulse discharge energy which in turn can improve the cutting rate 
Spark gap voltage  It is the specific value of voltage for the actual gap between the workpiece material and the wire  The MRR increases with increase in voltage and then starts to decrease 
wire feed  The rate at which the wire travels along the wire guide path and is fed for generating the sparks is called wire feed rate  The MRR remains nearly constant with variation in the wire feed. SR decrease with increase in wire feed rate, because new wire comes in contact rapidly when wire feed rate increases 
Wire tension  It determines the extension in wire between upper and lower wire guides  With increase in wire tension, the vibrations in wire reduces, which leads to reduction in inaccuracies during machining 
Dielectric pressure  It is the pressure of the dielectric fluid used in WEDM process  The increase in dielectric pressure carries more debris from the machining area resulting in reduction in SR 
It has been revealed after the review of literature that materials including alloys of metals like 16MnCr5, 20MnCr5 etc. have not been investigated yet and the research work regarding machining of these materials is limited up to a certain extent. Therefore, 16MnCr5 material has been selected for this research work to generate WEDM data. 16MnCr5 grade steel is low alloy chromium, manganese case hardening steel used extensively for carburizing and carbonitriding. This material is having high hardenability and excellent forgeability (Çetinkaya and Arabaci 2006). It has varied practical applications such as manufacturing of crankshaft, steering component, axels, gears, heat exchangers and power plant components. An investigation on machining of 16MnCr5 alloy steel by the WEDM process is necessary for understanding the performance characteristics. These data may be helpful for the operators which are working on WEDM of this material. Therefore, the present work was aimed to optimize the machining parameters in WEDM of 16MnCr5 alloy steel (workpiece) using Taguchi optimization technique.
2 Methods and materials
2.1 Workpiece material
Chemical composition of 16MnCr5 in percentage
Element  C  Cr  Mn  Al  P  S  Si 

Weight%  0.14–0.19  0.80–1.10  1.00–1.30  0.039 max  0.035 max  0.035 max  0.15–0.40 
2.2 Tool material
Diffused and the zinccoated wires were used in this experimental work as the tool electrodes. The wires were having a diameter of 0.25 mm. Diffused wire is widely used in WEDM due to its good machining properties like electric discharge performance, heat resistance, low clarification and heat release.
2.3 Methodology
Control parameters and their levels
Factor designation  Factors  Levels  DOF  

Level 1  Level 2  Level 3  
A  Type of wire (W_{t})  Diffused wire  Zinc Coated  _  1 
B  Pulse on time (T_{on})  115  120  125  2 
C  Pulse off time (T_{off})  40  45  50  2 
D  Peak current (I_{p})  180  200  220  2 
Constant parameters
Serial no.  Constant parameters  Selected value 

1  Workpiece material  16MnCr5 alloy steel 
2  Wire tension (Kg)  8 
3  Wire feed (m/min)  8 
4  Servo feed setting  2100 units 
5  Servo voltage (V)  20 
6  Dielectric fluid  Deionized water 
7  Flushing pressure  1 
2.4 Response variables
In the present research work, MINITAB17 software has been used for all the designs, plots and to carry out the analysis. In Taguchi designs, a measure of robustness is used to identify control factors that reduce variability in a process by minimizing the effects of uncontrollable factors (noise factors). Control factors are those process parameters that can be controlled. Noise factors cannot be controlled during production or product use, but can be controlled during experimentation. In a Taguchi designed experiment, noise factors can be manipulated to force variability to occur and from the results, identify optimal control factor settings that make the process robust, or resistant to variation from the noise factors. Higher values of the signaltonoise ratio (S/N) identify control factor settings that minimize the effects of the noise factors.
3 Results and discussion
L_{18} orthogonal array and experimental results
Exp. no.  Control factors and levels  MRR (mm^{3}/min)  S/N ratio, MRR (db)  SR (µm)  S/N ratio, SR (db)  

Wire type  T _{on}  T _{off}  I _{ p}  
1  1  1  1  1  11.490  21.2064  3.085  − 9.7851 
2  1  1  2  2  9.700  19.7354  3.525  − 10.9432 
3  1  1  3  3  6.790  16.6374  3.165  − 10.0075 
4  1  2  1  1  11.650  21.3265  3.445  − 10.7438 
5  1  2  2  2  13.560  22.6452  3.290  − 10.3439 
6  1  2  3  3  10.630  20.5307  3.115  − 9.8692 
7  1  3  1  2  14.090  22.9782  3.655  − 11.2577 
8  1  3  2  3  15.490  23.8010  3.590  − 11.1019 
9  1  3  3  1  13.560  22.6452  3.415  − 10.6678 
10  2  1  1  3  11.480  21.1988  2.910  − 9.2779 
11  2  1  2  1  10.360  20.3072  3.040  − 9.6575 
12  2  1  3  2  7.415  17.4022  3.000  − 9.5424 
13  2  2  1  2  12.970  22.2588  2.735  − 8.7391 
14  2  2  2  3  13.820  22.8102  3.380  − 10.5783 
15  2  2  3  1  10.640  20.5388  3.535  − 10.9678 
16  2  3  1  3  13.750  22.7661  3.265  − 10.2777 
17  2  3  2  1  15.150  23.6083  3.395  − 10.6168 
18  2  3  3  2  13.730  22.7534  3.630  − 11.1981 
3.1 Effect of different parameters on MRR
Figure 4 shows the main effect plots for S/N ratios for MRR. It is clear that the MRR increases with increase in pulse on time and decreases with increase in the peak current. This is due to the fact that time period between two successive sparks increases with an increase in pulse on time (Liao and Woo 1997). An increase in pulse on time leads to a maximum number of sparks and therefore more amount of energy is produced (Liao and Woo 1997). The MRR increases slightly with an increase in peak current. Peak current has a very little influence on the material removal rate as it increases slightly with an increase in peak current (Mishra et al. 2016).
ANOVA for MRR
Source  DF  Seq. SS  Adj. SS  Adj. MS  F  P 

Wire types  1  2.254  2.254  2.254  0.27  0.612 
Pulse on time  2  41.316  41.316  20.658  22.25  0.000 
Pulse off time  2  17.621  17.621  8.811  8.41  0.007 
Peak current  2  10.39  10.39  5.195  0.21  0.814 
Residual error  10  9.286  9.286  0.929  
Total  17  66.8672 
3.2 Time series plot for material removal rate (MRR)
3.3 Contour plots of material removal rate (MRR)
3.4 Confirmation tests for material removal rate (MRR)
After selecting optimum value of process parameters, improvement of the response parameters using the optimum level of process parameters is predicted and verified.
Confirmation experiment result of MRR
Initial cutting parameters  Optimal cutting parameters  

Prediction  Experiment  
Level  A1B3C3D1  A2B3C2D1  A2B3C2D1 
MRR (mm^{3}/min)  0.219  0.283  0.314 
S/N ratio (dB)  − 11.2304  − 10.9659  − 10.2181 
Improvement of S/N ratio  0.2645 
3.5 Effect of different parameters on SR
Analysis of Variance for SR
Source  DF  Seq. SS  Adj. SS  Adj. MS  F  P 

Wire types  1  1.83  1.83  1.83  2.14  0.175 
Pulse on time  2  4.87  4.87  2.44  3.86  0.049 
Pulse off time  2  2.96  2.96  1.48  1.12  0.364 
Peak current  2  1.35  1.35  0.68  0.20  0.824 
Residual error  10  3.88  3.88  0.39  
Total  17  8.74 
The data revealed that pulse on time was the most influential factor (contributing 46.79% to performance measure), followed by pulse off time (contributing 24.94%), wire types (contributing 16.47%) and peak current (contributing 6.52%). The factor with a p value less than 0.05 is counted significant due to 95% confidence level taken during analysis.
3.6 Time series plot for surface roughness (SR)
From Fig. 8, it can be seen that data show an upward trend with respect to the number of runs and there is a periodic fluctuation in the value of surface roughness on each run. The minimum value of SR is at a 13th run (2.735 µm) and the maximum value of SR is during the 7th run (3.655 µm).
3.7 Contour plot for surface roughness (SR)
3.8 Confirmation tests for surface roughness (SR)
Confirmation experiment result of SR
Initial cutting parameters  Optimal cutting parameters  

Prediction  Experiment  
Level  A2B2C3D1  A1B3C2D1  A1B3C2D1 
SR (µm)  3.415  3.7130  3.630 
S/N ratio (dB)  − 10.1547  − 11.3945  − 11.1019 
Improvement of S/N ratio  1.2398 
4 Conclusions

Pulse on time is the most significant parameter and thereafter order of significance being pulse off time and peak current in MRR.

MRR increases with increases in pulse on time. Since the energy released per spark increases with increase in pulse on time, hence higher time for each spark is provided leading to more material removal rate.

The optimal parameters for better material removal rate (MRR) are zinccoated electrode, pulse on time 125 µs, pulse off time 45 µs and peak current 180A.

Pulse on time is the most significant factors and thereafter the order of significance being pulse off time and types of the electrode in SR.

SR increases with increases in pulse on time. Discharge energy increases with increases in pulse on time due to this much more melting and resolidification of materials takes place leading to higher SR to be produced.

The optimal parameters for better surface finish are diffused electrode, pulse on time 125 µs, pulse off time 45 µs and peak current 180A.
Notes
Compliance with ethical standards
Conflict of interest
The authors have no conflict of interest and have not received any funding from any agency.
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